Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for distributing traffic load in a multi radio access technology heterogeneous network, the network comprising macrocells served by first base stations of a first tier of the network, each being referred to as a MBS, operating in a first frequency band, and minicells served by second base stations of a second tier of the network, each being referred to as a SBS, configured to operate in the first frequency band and in a second frequency band, separate from the first frequency band, wherein: coverage parameters of the network are acquired or measured, using the coverage parameters of the network, an optimum pair of bias values (Q T opt , Q R opt ) is determined that maximises a coverage probability, P c (γ), defined as a probability that a signal to noise and interference ratio on a terminal is on average greater than a predetermined threshold (γ) in a deployment zone of the network, a first base station MBS is associated with the terminal, if a strongest power received from the first base station MBS in the first frequency band is greater than a strongest power received from a second base station SBS in the same frequency band, corrected by a first bias value (Q T opt ) of the optimum pair of bias values (Q T opt , Q R opt ), the association then being carried out in the first frequency band, and otherwise, the second base station SBS of the strongest power received by the terminal in the first frequency band is associated with the terminal, the association being carried out in the first frequency band if the strongest power is greater than a power received by the terminal in the second frequency band, corrected by a second bias value (Q R opt ) of the optimum pair of bias values (Q T opt , Q R opt ), and the association being carried out in the second frequency band in the opposite case.
This invention relates to wireless communication networks and addresses the problem of efficiently distributing traffic load in a heterogeneous network composed of different types of base stations operating on different frequency bands. The network includes macrocells served by first base stations (MBS) operating in a first frequency band. It also includes minicells served by second base stations (SBS) that can operate in both the first frequency band and a separate second frequency band. The method involves acquiring or measuring network coverage parameters. Based on these parameters, an optimal pair of bias values (Q T opt, Q R opt) is determined. This pair is chosen to maximize the coverage probability, which is defined as the likelihood that a terminal's signal-to-noise-and-interference ratio (SINR) averages above a predefined threshold (γ) within the network's deployment area. A terminal is then associated with a base station based on received signal strengths and these bias values. Specifically, if the strongest signal received from an MBS in the first frequency band is greater than the strongest signal received from an SBS in the same first frequency band, adjusted by the first bias value (Q T opt), the terminal associates with the MBS in the first frequency band. Otherwise, if the strongest signal received from an SBS in the first frequency band is greater than the signal received from the SBS in the second frequency band, adjusted by the second bias value (Q R opt), the terminal associates with the SBS in the first frequency band. If neither of these conditions is met, the terminal associates with the SBS in the second frequency band.
2. The method for distributing traffic load according to claim 1 , wherein the first frequency band is a sub-6 GHz band from 0.7 GHz to 6 GHz, and the second frequency band is a millimeter band from 25 GHz to 300 GHz.
This invention relates to traffic load distribution in wireless communication systems, specifically addressing the challenge of efficiently managing data transmission across different frequency bands to optimize network performance. The method involves dynamically allocating traffic between a sub-6 GHz band (0.7 GHz to 6 GHz) and a millimeter-wave band (25 GHz to 300 GHz) based on network conditions. The sub-6 GHz band provides broader coverage and lower data rates, while the millimeter-wave band offers higher data rates but with limited range. The system monitors factors such as signal strength, interference levels, and user device capabilities to determine the optimal distribution of traffic between these bands. By dynamically adjusting the allocation, the method ensures efficient use of available spectrum, reduces congestion, and enhances overall network throughput. The approach leverages the complementary strengths of both frequency ranges to improve reliability and user experience in dense or high-traffic environments. The solution is particularly useful in 5G and beyond networks, where diverse frequency bands are utilized to meet varying demand and coverage requirements.
3. The method for distributing traffic load according to claim 2 , wherein the coverage probability, P c (γ), is calculated from P c ( γ ) = ∑ t ∈ { M , S } v ∈ { L , N } r ∈ { μ , m } P ( SINR > γ | t , v , r ) P tvr , where P tvr is a probability of association of the terminal with a given base station of tier t ∈{M,S} where M designates the first tier of the network and S designates the second tier of the network, in conditions of visibility v∈{L,N} where L designates a state of visibility LOS and N designates a state of visibility NLOS, and in frequency band r ∈{μ,m} where μ designates the sub-6 GHz band and m designates the millimeter band, and where P(SINR>γ|t, v, r) is a conditional probability that the signal to noise and interference ratio on the terminal exceeds the predetermined threshold, γ.
This invention relates to traffic load distribution in wireless communication networks, specifically optimizing load balancing across multiple network tiers, visibility conditions, and frequency bands. The problem addressed is efficiently distributing traffic to maximize coverage and capacity while accounting for varying signal conditions. The method calculates a coverage probability, Pc(γ), which quantifies the likelihood that a terminal's signal-to-noise-and-interference ratio (SINR) exceeds a predefined threshold, γ. The calculation integrates probabilities across three key dimensions: network tier (macro or small cell), visibility state (line-of-sight or non-line-of-sight), and frequency band (sub-6 GHz or millimeter wave). The probability of a terminal associating with a specific base station, P_tvr, is combined with the conditional SINR probability, P(SINR>γ|t, v, r), to derive Pc(γ). This approach enables dynamic load distribution by considering the combined effects of network architecture, propagation conditions, and spectral characteristics, improving overall network performance and reliability. The method supports adaptive traffic management in heterogeneous networks with diverse deployment scenarios.
4. The method for distributing traffic load according to claim 3 , wherein the probability P tvr , of association of the terminal with the given base station of tier t ∈{M,S}, in conditions of visibility v ∈{L, N} and in frequency band r ∈{μ,m} is calculated with P tvr =P tv P vr , where P tv is a probability of association of the terminal with the given base station of tier t ∈{M,S}, in conditions of visibility v ∈{L,N} and P vr is a probability of association of the terminal with a second base station SBS of visibility P vr in the frequency band r.
This invention relates to traffic load distribution in wireless communication networks, specifically addressing the challenge of efficiently managing terminal associations with base stations across different tiers and visibility conditions. The method calculates the probability of a terminal associating with a given base station based on multiple factors, including the base station's tier (macro or small cell), visibility conditions (line-of-sight or non-line-of-sight), and frequency band (microwave or millimeter-wave). The probability is determined by decomposing it into two components: the probability of association with the base station under specific visibility conditions and the probability of association with a second small base station in the same frequency band. This approach allows for dynamic load balancing by adjusting terminal associations to optimize network performance and resource utilization. The method ensures that traffic is distributed efficiently across the network, reducing congestion and improving overall system capacity. The solution is particularly useful in heterogeneous networks where multiple base stations operate in different frequency bands and visibility scenarios.
6. The method for distributing traffic load according to claim 4 , wherein the probability P vr is calculated by P v μ = exp ( - π λ S ( K Svm G 0 Q R K Sv μ ) 2 α Svm - α SV μ ) for an association with a second base station SBS in the sub-6 GHz band and P vm =1−P vμ for an association with a second base station SBS in the millimeter band, where K Svm and K Svμ are respectively the respective path loss constants for the millimeter band and the sub-6 GHz band in a state of visibility v, α Svm and α Svμ are the respective path loss exponents for the millimeter band and the sub-6 GHz band in a state of visibility v, G 0 is the antenna gain, λ S , is the intensity of a Poisson distribution giving a spatial distribution of the second base stations SBS, and Q R is a second bias value used for selection of the frequency band in the association of the terminal with a second base station SBS.
This invention relates to wireless communication systems, specifically methods for distributing traffic load between sub-6 GHz and millimeter-wave (mmWave) frequency bands in heterogeneous networks. The problem addressed is efficient load balancing between these bands to optimize network performance, particularly in scenarios where visibility conditions (e.g., line-of-sight or non-line-of-sight) affect signal propagation. The method calculates association probabilities for a terminal device connecting to a small base station (SBS) in either the sub-6 GHz or mmWave band. For sub-6 GHz associations, the probability P_vμ is determined using an exponential function incorporating path loss constants (K_Svμ), path loss exponents (α_Svμ), antenna gain (G_0), and a spatial distribution parameter (λ_S) derived from a Poisson process. A bias value (Q_R) is applied to influence band selection. For mmWave associations, the probability P_vm is the complement of P_vμ (P_vm = 1 - P_vμ). The path loss constants and exponents differ between bands, accounting for their distinct propagation characteristics. The spatial distribution parameter models the density of SBSs, while the bias value allows network operators to prioritize one band over the other. This approach dynamically adjusts load distribution based on environmental conditions and network configuration, improving overall efficiency and reliability.
7. The method for distributing traffic load according to claim 3 , wherein the conditional probability P(SINR>γ|t, v, μ) that the signal to noise and interference ratio on the terminal exceeds the predetermined threshold, knowing that the terminal is associated with the given base station of tier t, of state of visibility v, operating in the sub-6 GHz band, is obtained from the probability density of the given base station of tier t, of state of visibility v and of the strongest power received, as well as a measurement of power received from the given base station.
This invention relates to wireless communication systems, specifically methods for distributing traffic load among base stations to optimize network performance. The problem addressed is efficiently managing traffic distribution in heterogeneous networks where base stations operate in the sub-6 GHz band, ensuring reliable signal quality for terminals while balancing load across different tiers of base stations. The method calculates a conditional probability that the signal-to-noise-and-interference ratio (SINR) at a terminal exceeds a predetermined threshold, given the terminal's association with a specific base station. The base station is characterized by its tier (e.g., macro, micro, pico), visibility state (e.g., line-of-sight or non-line-of-sight), and the strongest received power. The probability is derived from the probability density function of the base station's tier, visibility state, and the strongest received power, combined with actual power measurements from the base station. This allows the network to predict signal quality and make informed decisions about traffic distribution, ensuring optimal performance and resource utilization. The approach helps mitigate interference and improve reliability in dense wireless environments.
8. The method for distributing traffic load according to claim 3 , wherein the conditional probability P (SINR>λ|S, v, m) that the signal to noise and interference ratio on the terminal exceeds the predetermined threshold, knowing that the terminal is associated with a second base station SBS, of state of visibility v, operating in the millimeter band, is obtained from the probability density of the second base station SBS of the strongest power received from the second base station SBS, from antenna gains of the terminal and from the second base station SBS as well as from angular widths of main lobes of radiation of the terminal and of the second base station SBS.
This invention relates to traffic load distribution in wireless communication networks, particularly for millimeter-wave (mmWave) systems where signal propagation is highly directional and sensitive to environmental conditions. The problem addressed is efficiently distributing traffic load among base stations to optimize network performance while accounting for signal quality and interference. The method calculates a conditional probability that the signal-to-interference-plus-noise ratio (SINR) at a terminal exceeds a predefined threshold, given that the terminal is connected to a small base station (SBS) operating in the mmWave band. This probability is derived from multiple factors, including the probability density of the strongest received power from the SBS, antenna gains of both the terminal and the SBS, and the angular widths of the main lobes of their radiation patterns. By incorporating these parameters, the method assesses the likelihood of maintaining a high-quality link under varying conditions, enabling better load balancing decisions. The approach ensures that traffic is distributed based on signal reliability, reducing the risk of dropped connections or degraded performance in mmWave networks. This is particularly useful in dense urban environments where mmWave signals are prone to blockage and interference.
9. The method for distributing traffic load according to claim 3 , wherein the coverage probability is calculated as a function P c (γ)=F (G 0 , Q T , Q R ), where G 0 is the antenna gain, a product of the receiving antenna gain of the terminal and of a transmitting antenna gain of a second station SBS, and Q T , Q R are the first and the second bias values.
This invention relates to traffic load distribution in wireless communication systems, specifically addressing the challenge of efficiently balancing network load while maintaining reliable coverage. The method calculates a coverage probability function Pc(γ) = F(G0, QT, QR) to determine optimal traffic distribution. Here, G0 represents the combined antenna gain of a terminal and a second station (SBS), accounting for both receiving and transmitting gains. QT and QR are bias values applied to adjust the distribution of traffic load between different network nodes. The coverage probability function evaluates how likely a terminal will maintain a stable connection under varying conditions, enabling dynamic load balancing. By incorporating antenna gains and bias values, the method ensures that traffic is distributed based on both physical signal strength and network policy preferences. This approach improves network efficiency by preventing overloading of specific nodes while maintaining service quality. The solution is particularly useful in heterogeneous networks where multiple base stations or access points must coordinate to handle varying traffic demands. The method dynamically adapts to changes in network conditions, optimizing resource allocation without requiring manual intervention.
10. The method for distributing traffic load according to claim 9 , wherein the optimum pair of bias values (Q T opt , Q R opt ) is obtained by systematically sweeping a Cartesian product S QT ×S QR where S QT is a set of first possible bias values and S QR is a set of second possible bias values, and by searching for a pair of first and second bias values that maximises the function F (G 0 ,Q T , Q R ).
This invention relates to traffic load distribution in network systems, specifically optimizing bias values to improve load balancing performance. The problem addressed is efficiently determining optimal bias values for traffic distribution to maximize a performance function, such as throughput or fairness, in a network with multiple nodes or paths. The method involves systematically evaluating pairs of bias values (Q_T, Q_R) by sweeping through a Cartesian product of predefined sets of possible values (S_QT and S_QR). For each pair, the method calculates a performance function F(G_0, Q_T, Q_R), where G_0 represents a baseline or initial network state. The pair (Q_T_opt, Q_R_opt) that maximizes this function is selected as the optimal bias values for traffic distribution. The performance function F may incorporate metrics like traffic load, latency, or resource utilization, ensuring the chosen bias values enhance overall network efficiency. The systematic sweep ensures exhaustive evaluation of possible bias combinations, while the optimization process dynamically adapts to network conditions. This approach is particularly useful in dynamic environments where traffic patterns or network topology changes frequently, requiring real-time adjustment of bias values to maintain optimal performance.
11. The method for distributing traffic load according to claim 9 , wherein the optimum pair of bias values (Q T opt , Q R opt ) is obtained by calculating Q R opt = E [ S m I m + σ N , m 2 ] E [ S μ I μ + σ N , μ 2 ] , where S m I m + σ N , m 2 is the signal to noise ratio on the terminal in the S μ I μ + σ N , μ 2 millimeter band, is the signal to noise ratio on the terminal in the sub-6 GHz band, and E is the mathematical expectation taken over all possible positions of the terminal with respect to base stations of the network.
This invention relates to optimizing traffic load distribution in wireless networks, particularly for systems operating in both millimeter-wave (mmWave) and sub-6 GHz bands. The problem addressed is efficiently balancing traffic between these bands to maximize network performance while accounting for varying signal conditions. The method calculates an optimal pair of bias values (Q_T_opt, Q_R_opt) to determine how traffic should be allocated between the bands. The bias value for the sub-6 GHz band (Q_R_opt) is derived from the ratio of the expected signal-to-noise ratios (SNR) in the mmWave and sub-6 GHz bands. The SNR for each band is calculated as the sum of the mean signal power (S) and the interference power (I), plus the noise variance (σ_N^2), averaged over all possible terminal positions relative to base stations. This approach ensures that traffic is distributed based on real-world signal conditions, improving overall network efficiency and reliability. The method dynamically adjusts bias values to adapt to changing environmental factors, such as terminal movement or interference fluctuations, ensuring optimal performance across different network scenarios.
12. The method for distributing traffic load according to claim 9 , wherein the second base stations SBS operate only in the millimeter band, and the optimum pair of bias values (Q T opt , Q R opt ) is obtained by applying a gradient descent to the function F (G 0 ,Q T ,Q R ).
This invention relates to traffic load distribution in wireless communication networks, specifically addressing the challenge of efficiently managing load across base stations operating in different frequency bands, particularly millimeter-wave (mmWave) bands. The method involves optimizing bias values for traffic steering between a macro base station (MBS) and small base stations (SBSs) to balance load and improve network performance. The SBSs operate exclusively in the millimeter band, which offers high data rates but has limited coverage due to propagation characteristics. The method calculates an optimal pair of bias values (Q_T_opt, Q_R_opt) by applying a gradient descent algorithm to a function F(G_0, Q_T, Q_R), where G_0 represents a baseline metric, and Q_T and Q_R are bias values for traffic offloading and reception, respectively. The gradient descent iteratively adjusts these bias values to minimize a cost function, ensuring efficient load distribution while maintaining service quality. This approach dynamically adapts to varying network conditions, improving resource utilization and user experience in heterogeneous networks with mixed-frequency deployments. The solution is particularly useful in dense urban environments where mmWave SBSs are deployed alongside traditional macro cells to enhance capacity and coverage.
13. The method for distributing traffic load according to claim 1 , wherein, in order to transfer a terminal associated with a second base station SBS, from the sub-6 GHz band to the millimeter band, an antenna gain of the terminal is increased, G RX,m UE , in the millimeter band, such that G RX , m UE = G RX , μ UE G TX , μ SBS G TX , m SBS ( K SL μ · P SL μ K SLm · P SLm ) d ( α SLm - α SL μ ) , where G RX,μ UE is the antenna gain of the terminal in the sub-6 GHz band G TX,μ SBS and G TX,m SBS are the antenna gains of the second base station SBS associated with the terminal, respectively in the sub-6 GHz band and in the millimeter band, K SLμ and K SLm are respectively the path loss constants in the sub-6 GHz band and in the millimeter band, P SLμ and P SLm are respectively the powers transmitted by the second base station SBS in the sub-6 GHz band and in the millimeter band, α SLμ and α SLm are respectively the path loss exponents in the sub-6 GHz band and in the millimeter band, for a direct propagation path between the second base station SBS associated with the terminal, and d is the distance between the second base station SBS and the terminal.
This invention relates to wireless communication systems, specifically methods for distributing traffic load between sub-6 GHz and millimeter-wave (mmWave) bands to optimize network performance. The problem addressed is the efficient handover of terminals from sub-6 GHz to mmWave bands while maintaining signal quality and minimizing interference. The solution involves dynamically adjusting the terminal's antenna gain in the mmWave band to compensate for differences in propagation characteristics between the two frequency bands. The antenna gain adjustment is calculated using a formula that accounts for the terminal's existing sub-6 GHz gain, the base station's transmit gains in both bands, path loss constants, transmitted powers, path loss exponents, and the distance between the terminal and the base station. This ensures seamless transition and reliable communication in the mmWave band, which typically suffers from higher path loss and more severe signal attenuation. The method helps balance traffic load across bands, improving overall network efficiency and capacity.
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May 12, 2020
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